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My research focuses on understanding how the brain uses visual and arousal information in decision making and learning.

Reward-based reinforcement learning provides a systematic understanding of how adaptive behavior maximizes benefits. The basic process involves choosing a behavior for which the maximal reward is expected and revising the prediction so as to minimize the reward prediction error, which is the difference between the predicted and actual reward. We have suggested that the Pedunculopontine Tegmental Nucleus (PPTN) is the key structure for the reward prediction error computation. In our research, we typically record the activity of PPTN neurons in monkeys performing saccade tasks for juice reward. One of our key findings is that we have identified two groups of PPTN neurons that are selectively responsive to reward cue and reward delivery, which are the necessary and sufficient pieces of information required for prediction error computation.

More recently, we have begun studying the neurophysiological effects of transcranial magnetic and current stimulation, to try and build a deeper understanding of brain dynamics and function during decision-making.